Publications
2017
1.
González-Castro, Víctor; del Carmen Valdés-Hernández, María; Chappell, Francesca M; Armitage, Paul A; Makin, Stephen; Wardlaw, Joanna M
Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance Artículo de revista
En: Clinical Science, vol. 131, no 13, pp. 1465–1481, 2017, (Publisher: Portland Press Ltd.).
Resumen | Enlaces | BibTeX | Etiquetas: Bag of Visual Words, brain MRI, Discrete Wavelet Transform, Local Binary Patterns, machine learning, perivascular spaces, small vessel disease, support vector machine
@article{gonzalez-castro_reliability_2017,
title = {Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance},
author = {Víctor González-Castro and María del Carmen Valdés-Hernández and Francesca M Chappell and Paul A Armitage and Stephen Makin and Joanna M Wardlaw},
url = {https://portlandpress.com/clinsci/article/131/13/1465/71656/Reliability-of-an-automatic-classifier-for-brain},
year = {2017},
date = {2017-01-01},
journal = {Clinical Science},
volume = {131},
number = {13},
pages = {1465–1481},
abstract = {Enlarged perivascular spaces (PVS) in the brain are associated with small vessel disease, poor cognition, and hypertension. This study proposes a fully automated method using a support vector machine (SVM) to classify PVS burden in the basal ganglia (BG) as low or high from T2-weighted MRI images. Three feature extraction techniques were evaluated, with the bag of visual words (BoW) approach achieving the highest accuracy (81.16%). The classifier's performance was comparable to that of trained human observers, and its predictions were clinically meaningful, as indicated by high AUC values (0.90–0.93). These findings suggest that automated PVS burden assessment could serve as a valuable clinical tool.},
note = {Publisher: Portland Press Ltd.},
keywords = {Bag of Visual Words, brain MRI, Discrete Wavelet Transform, Local Binary Patterns, machine learning, perivascular spaces, small vessel disease, support vector machine},
pubstate = {published},
tppubtype = {article}
}
Enlarged perivascular spaces (PVS) in the brain are associated with small vessel disease, poor cognition, and hypertension. This study proposes a fully automated method using a support vector machine (SVM) to classify PVS burden in the basal ganglia (BG) as low or high from T2-weighted MRI images. Three feature extraction techniques were evaluated, with the bag of visual words (BoW) approach achieving the highest accuracy (81.16%). The classifier's performance was comparable to that of trained human observers, and its predictions were clinically meaningful, as indicated by high AUC values (0.90–0.93). These findings suggest that automated PVS burden assessment could serve as a valuable clinical tool.
2016
2.
Ballerini, Lucía; Lovreglio, Ruggiero; del Carmen Valdés-Hernández, María; González-Castro, Víctor; Muñoz-Maniega, Susana; Pellegrini, Enrico; Bastin, Mark E; Deary, Ian J; Wardlaw, Joanna M
Application of the ordered logit model to optimising Frangi filter parameters for segmentation of perivascular spaces Artículo de revista
En: Procedia Computer Science, vol. 90, pp. 61–67, 2016, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: brain MRI, frangi filter, medical imaging, neuroimaging, perivascular spaces
@article{ballerini_application_2016,
title = {Application of the ordered logit model to optimising Frangi filter parameters for segmentation of perivascular spaces},
author = {Lucía Ballerini and Ruggiero Lovreglio and María del Carmen Valdés-Hernández and Víctor González-Castro and Susana Muñoz-Maniega and Enrico Pellegrini and Mark E Bastin and Ian J Deary and Joanna M Wardlaw},
url = {https://www.sciencedirect.com/science/article/pii/S1877050916311899},
year = {2016},
date = {2016-01-01},
journal = {Procedia Computer Science},
volume = {90},
pages = {61–67},
abstract = {Segmenting perivascular spaces (PVS) in brain MRI is crucial for studying the brain's lymphatic system and its link to neurological diseases. The Frangi filter is a useful tool for this task, but its parameters must be optimized for different scanner settings. This study employs an ordered logit model to refine these parameters based on neuroradiological PVS ratings. The resulting PVS volume strongly correlates with expert assessments (Spearman’s ρ=0.75, p < 0.001), indicating that this approach is a promising alternative to conventional optimization methods.},
note = {Publisher: Elsevier},
keywords = {brain MRI, frangi filter, medical imaging, neuroimaging, perivascular spaces},
pubstate = {published},
tppubtype = {article}
}
Segmenting perivascular spaces (PVS) in brain MRI is crucial for studying the brain's lymphatic system and its link to neurological diseases. The Frangi filter is a useful tool for this task, but its parameters must be optimized for different scanner settings. This study employs an ordered logit model to refine these parameters based on neuroradiological PVS ratings. The resulting PVS volume strongly correlates with expert assessments (Spearman’s ρ=0.75, p < 0.001), indicating that this approach is a promising alternative to conventional optimization methods.
2015
3.
Viksne, Linda; del Carmen Valdés-Hernández, María; Hoban, Katie; Heye, Anna K; González-Castro, Víctor; Wardlaw, Joanna M
Textural Characterisation on Regions of Interest: A Useful Tool for the Study of Small Vessel Disease. Artículo de revista
En: MIUA, pp. 66–71, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: brain MRI, Stroke analysis, SVD, Textural Features
@article{viksne_textural_2015,
title = {Textural Characterisation on Regions of Interest: A Useful Tool for the Study of Small Vessel Disease.},
author = {Linda Viksne and María del Carmen Valdés-Hernández and Katie Hoban and Anna K Heye and Víctor González-Castro and Joanna M Wardlaw},
url = {https://d1wqtxts1xzle7.cloudfront.net/47501590/th_Conference_on_Medical_Image_Analysis_20160725-1021-168or6i-libre.pdf?1469454941=&response-content-disposition=inline%3B+filename%3D19th_Conference_on_Medical_Image_Analysi.pdf&Expires=1739800144&Signature=eq0D4hNfRml9ojmCa4JiJDFaIaZSf~C1rvuQlV5SsFmM5Q6mhYopnVsB162WyvtWERcmw0whBHUT5AJdjDouXL1PfU~oagJN7rwthynxQBQFERLKiX6j~UNlRyK-vpLJuawjpU~y8FLLHEaUlkGx8D4zYbippq6L0ZpRdAiyRRSvGufGL-hceswY~25tbSVvbb~a9xrNI0AlxO73eudMSTunozIKtV1JxnlPRlv3ARafGvFWFj1zI23HQh4d~EHKvBTxpn-CwDCboXuRJA5wjkl8AyaFqLui7mmo7jlL3L5ZIiasJJlKuJFLkgA05qz9ZdPKyikMzKXcAH4EO9s-9g__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2015},
date = {2015-01-01},
journal = {MIUA},
pages = {66–71},
abstract = {This study proposes a framework to investigate the properties of normal brain tissues in patients with small vessel disease (SVD) using structural MRI. The approach extracts textural features from regions of interest (ROIs) using anatomically-relevant templates and combines statistical analysis of SVD markers (e.g., microbleeds, perivascular spaces) relative to these features. The study, based on data from 42 patients with mild stroke, shows that normal tissues are heterogeneous and local variations (measured by entropy) correlate with SVD markers, aligning with clinical findings.},
keywords = {brain MRI, Stroke analysis, SVD, Textural Features},
pubstate = {published},
tppubtype = {article}
}
This study proposes a framework to investigate the properties of normal brain tissues in patients with small vessel disease (SVD) using structural MRI. The approach extracts textural features from regions of interest (ROIs) using anatomically-relevant templates and combines statistical analysis of SVD markers (e.g., microbleeds, perivascular spaces) relative to these features. The study, based on data from 42 patients with mild stroke, shows that normal tissues are heterogeneous and local variations (measured by entropy) correlate with SVD markers, aligning with clinical findings.